Two-dimensional microscanners with t-shaped hinges and piezoelectric actuators

For a wide range of application areas such as medical instruments, defense, communication networks, industrial equipment, and consumer electronics, microscanners have been a vibrant research topic. Among various fabrication methodologies, MEMS (microelectromechanical system) stands out for its small size and fast response characteristics. In this thesis, piezoelectric actuation mechanism is selected because of its low voltage and low current properties compared with other mechanisms, which are especially important for the target application of biomedical imaging. Although 1- and 2-dimensional microscanners with piezoelectric actuators have been studied by several other groups, this thesis introduces innovative improvements in design of the piezoelectric MEMS microscanner. A novel T-shaped hinge geometry is proposed, which is flexible in whole six directions and also free from the crosstalk issue found in the earlier designs by other groups. The piezoelectric actuator of the microscanner is comprised of five layers; a top electrode, a piezoelectric layer (lead zirconate titanate or PZT), a bottom electrode, a dielectric layer, and a mechanical support. The microscanners were analyzed using both analytical formulas and numerical simulations. Based on the analysis, the microscanners were designed and fabricated with four mask levels¯top electrodes, bottom electrodes, bonding pads, and substrate etching windows. During the silicon substrate wet etching process in KOH, ProTEK@ B3 was coated in the front to protect the devices. Polarization-voltage (P-V) measurement of deposited PZT was performed using RT66B. Actuation of the piezoelectric cantilevers were observed under a microscope by applying voltage

Does Japanese encephalitis virus share the same cellular receptor with other mosquito-borne flaviviruses on the C6/36 mosquito cells?

Japanese encephalitis virus (JEV) is a member of mosquito-borne Flaviviridae. To date, the mechanisms of the early events of JEV infection remain poorly understood, and the cellular receptors are unidentified. There are evidences that the structure of the virus attachment proteins (VAP), envelope glycoprotein of mosquito-borne flaviviruses is very similar, and the vector-virus interaction of mosquito-borne flaviviruses is also very similar. Based on the studies previously demonstrated that the similar molecules present on the mosquito cells involved in the uptake process of JEV, West Nile virus (WNV) and Dengue virus (DV), it is proposed that the same receptor molecules for mosquito-borne flaviviruses (JEV, WNV and DV) may present on the surface of C6/36 mosquito cells. By co-immunoprecipitation assay, we investigated a 74-KDa protein on the C6/36 cells binds JEV, and the mass spectrometry results indicated it may be heat shock cognate protein 70(HSC70) from Aedes aegypti. Based upon some other viruses use of heat shock protein 70 (HSP70) family proteins as cell receptors, its possible HSC70's involvement in the fusion of the JEV E protein with the C6/36 cells membrane, and known form of cation channels in the interaction of HSC70 with the lipid bilayer, it will further be proposed that HSC70 as a penetration receptor mediates JEV entry into C6/36 cells

Dynamic Kernel Sparsifiers

A geometric graph associated with a set of points $P= \{x_1, x_2, \cdots, x_n \} \subset \mathbb{R}^d$ and a fixed kernel function $\mathsf{K}:\mathbb{R}^d\times \mathbb{R}^d\to\mathbb{R}_{\geq 0}$ is a complete graph on $P$ such that the weight of edge $(x_i, x_j)$ is $\mathsf{K}(x_i, x_j)$. We present a fully-dynamic data structure that maintains a spectral sparsifier of a geometric graph under updates that change the locations of points in $P$ one at a time. The update time of our data structure is $n^{o(1)}$ with high probability, and the initialization time is $n^{1+o(1)}$. Under certain assumption, we can provide a fully dynamic spectral sparsifier with the robostness to adaptive adversary. We further show that, for the Laplacian matrices of these geometric graphs, it is possible to maintain random sketches for the results of matrix vector multiplication and inverse-matrix vector multiplication in $n^{o(1)}$ time, under updates that change the locations of points in $P$ or change the query vector by a sparse difference

LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation

Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV feature pyramid decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map.Comment: Accepted by CVPR202